Energy-Efficient Uncertainty-Aware Biomass Composition Prediction at the Edge

• URL: http://arxiv.org/abs/2404.11230v1
• Date: Wed, 17 Apr 2024 10:26:49 GMT
• Title: Energy-Efficient Uncertainty-Aware Biomass Composition Prediction at the Edge
• Authors: Muhammad Zawish, Paul Albert, Flavio Esposito, Steven Davy, Lizy Abraham,
• Abstract summary: Herbage containing clover promotes higher food intake, resulting in higher milk production. Deep learning algorithms have been proposed with the goal to estimate the dry biomass composition from images of the grass directly in the fields. This paper proposes to fill this gap by applying filter pruning to reduce the energy requirement of existing deep learning solutions. We address this challenge by training filter-pruned models using a variance attenuation loss so they can predict the uncertainty of their predictions. When the uncertainty exceeds a threshold, we re-infer using a more accurate unpruned model.
• Score: 6.76163770004542
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Clover fixates nitrogen from the atmosphere to the ground, making grass-clover mixtures highly desirable to reduce external nitrogen fertilization. Herbage containing clover additionally promotes higher food intake, resulting in higher milk production. Herbage probing however remains largely unused as it requires a time-intensive manual laboratory analysis. Without this information, farmers are unable to perform localized clover sowing or take targeted fertilization decisions. Deep learning algorithms have been proposed with the goal to estimate the dry biomass composition from images of the grass directly in the fields. The energy-intensive nature of deep learning however limits deployment to practical edge devices such as smartphones. This paper proposes to fill this gap by applying filter pruning to reduce the energy requirement of existing deep learning solutions. We report that although pruned networks are accurate on controlled, high-quality images of the grass, they struggle to generalize to real-world smartphone images that are blurry or taken from challenging angles. We address this challenge by training filter-pruned models using a variance attenuation loss so they can predict the uncertainty of their predictions. When the uncertainty exceeds a threshold, we re-infer using a more accurate unpruned model. This hybrid approach allows us to reduce energy consumption while retaining a high accuracy. We evaluate our algorithm on two datasets: the GrassClover and the Irish clover using an NVIDIA Jetson Nano edge device. We find that we reduce energy reduction with respect to state-of-the-art solutions by 50% on average with only 4% accuracy loss.

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